Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure

Abstract

The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.

Physical Activity, Sedentary Time, and Cardiovascular Disease Biomarkers at Age 60 to 64 Years

“…Heart rate and movement data were recorded in 30‐s epochs to derive measures of free‐living PA. Heart rate data were preprocessed33 and then individually calibrated using an 8‐minute step test to account for between‐individual differences in the relationship between PA intensity and heart rate34; group calibration was used where individual calibration was not carried out, adjusted only for sleeping heart rate, age, sex, and β‐blocker use 34. Total PA energy expenditure (PAEE) (in kJ/kg per day) was derived using a branched equation framework35 as recently validated in a UK population36 from which time spent at different PA intensities was summarized; these were based on metabolic equivalent of tasks (METs) as time spent sedentary (<1.5 METs) and in LPA (1.5–3 METs) and MVPA (>3 METs), using 1 standard MET (20.35 J/ mL O 2 ×3.5 mL O 2 / min per kg) as resting metabolic rate.…”

Physical activity energy expenditure vs cardiorespiratory fitness level in impaired glucose metabolism

“…This method made it possible to estimate activity intensity or instantaneous energy expenditure above rest, which when integrated over time yields average daily PAEE or total volume of activity. Both the activity volume and intensity estimate have been shown to be more valid than estimates based on either heart rate or accelerometry alone, in both experimental and free-living conditions as compared with indirect calorimetry and doubly-labelled water, particularly when heart rate is individually calibrated [23,24,[46][47][48]. However, both PAEE and CRF reflect multidimensional activities with an inherent variability, which makes their estimation difficult, particularly for PAEE [34].…”

“…PAEE To measure the physical activity of the participants, a combined accelerometer and heart rate monitor (ActiHeart, CamNTech, Cambridge, UK) was used [23]. The monitor measures accelerations (recorded as counts) and heart rate independently.…”

Oxyntomodulin increases energy expenditure in addition to decreasing energy intake in overweight and obese humans: a randomised controlled trial

“…This software used branched equation modelling to derive 'free-living' daily energy expenditure from movement and heart rate parameters. 23 Each subject had individual calibration of the Actiheart monitor data by input of their measured resting energy expenditure into the model. The subjects also performed a graded exercise test on day 1 of each study session in which they were asked to repeatedly mount a step of height 220 mm, at a speed of 15 steps per minute increasing to 33 steps per minute, over an 8 min period.…”